Add the max-pool2d quantization support and the partial quantization support. (#19310)

* add pool2d quantization support, only for max-pooling.

* add the partial quantization support.

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -26,6 +26,23 @@ __all__ = [
     'AddQuantDequantPass'
 ]
 
+_quantizable_op_list = ['conv2d', 'depthwise_conv2d', 'mul', 'pool2d']
+
+_fake_quant_op_list = [
+    'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
+    'fake_quantize_moving_average_abs_max', 'fake_channel_wise_quantize_abs_max'
+]
+
+_fake_dequant_op_list = [
+    'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
+]
+
+_out_scale_op_list = [
+    "mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid", "depthwise_conv2d",
+    "batch_norm", "concat", "tanh", "pad", "elementwise_add", "elementwise_mul",
+    "dropout", "split", "prelu", "conv2d_transpose", "leaky_relu"
+]
+
 
 def _init_var_node(var_node, value, scope, place):
     assert isinstance(value,
@@ -47,7 +64,8 @@ class QuantizationTransformPass(object):
                  activation_quantize_type='abs_max',
                  weight_quantize_type='abs_max',
                  window_size=10000,
-                 moving_rate=0.9):
+                 moving_rate=0.9,
+                 skip_pattern='skip_quant'):
         """
         Convert and rewrite the IrGraph according to weight and
         activation quantization type.
@@ -92,6 +110,7 @@ class QuantizationTransformPass(object):
         self._place = place
         self._weight_bits = weight_bits
         self._activation_bits = activation_bits
+        self._skip_pattern = skip_pattern
 
         quant_type = [
             'abs_max', 'channel_wise_abs_max', 'range_abs_max',
@@ -114,7 +133,7 @@ class QuantizationTransformPass(object):
         self._window_size = window_size
         self._moving_rate = moving_rate
 
-        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+        self._quantizable_ops = _quantizable_op_list
         self._conv_ops = ['conv2d', 'depthwise_conv2d']
         self._quantizable_grad_ops = [
             '%s_grad' % (op) for op in self._quantizable_ops
@@ -138,6 +157,16 @@ class QuantizationTransformPass(object):
         dequantized_vars = collections.OrderedDict()
         persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
 
+        def _quant_preprocess(op_node):
+            pool_skipped = op_node.op().has_attr("pooling_type") and \
+                    op_node.op().attr("pooling_type") == 'avg'
+            user_skipped = isinstance(self._skip_pattern, str) and \
+                           op_node.op().has_attr("op_namescope") and \
+                           op_node.op().attr("op_namescope").find(self._skip_pattern) != -1
+
+            if pool_skipped or user_skipped:
+                op_node.op()._set_attr("skip_quant", True)
+
         def _transform_forward(graph, op):
             for var_node in op.inputs:
                 if var_node.name() not in op.input_arg_names():
@@ -188,14 +217,28 @@ class QuantizationTransformPass(object):
         if not self._is_test:
             self._create_global_step(graph)
         ops = graph.all_op_nodes()
+        # Do the preproccess of quantization, such as skipping some ops
+        # for not being quantized.
+        for op in ops:
+            if op.name() in self._quantizable_ops or \
+                    op.name() in self._quantizable_grad_ops:
+                _quant_preprocess(op)
         # The process of _transform_forward and _transform_backward is needed in two for loops.
         # The loop for transforming the forward graph:
         for op in ops:
             if op.name() in self._quantizable_ops:
+                skipped = op.op().has_attr("skip_quant") and \
+                         op.op().attr("skip_quant")
+                if skipped:
+                    continue
                 _transform_forward(graph, op)
         # The loop for renaming the inputs of backward op.
         for op in ops:
             if op.name() in self._quantizable_grad_ops:
+                skipped = op.op().has_attr("skip_quant") and \
+                         op.op().attr("skip_quant")
+                if skipped:
+                    continue
                 _transform_backward(graph, op)
         graph.resolve_hazard()
         return graph
@@ -571,16 +614,10 @@ class QuantizationFreezePass(object):
         self._weight_bits = weight_bits
         self._activation_bits = activation_bits
         self._weight_quantize_type = weight_quantize_type
-        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+        self._quantizable_ops = _quantizable_op_list
         self._conv_ops = ['conv2d', 'depthwise_conv2d']
-        self._fake_quant_op_names = [
-            'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
-            'fake_quantize_moving_average_abs_max',
-            'fake_channel_wise_quantize_abs_max'
-        ]
-        self._fake_dequant_op_names = [
-            'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
-        ]
+        self._fake_quant_op_names = _fake_quant_op_list
+        self._fake_dequant_op_names = _fake_dequant_op_list
         self._op_input_rename_map = collections.OrderedDict()
         self._op_output_rename_map = collections.OrderedDict()
         self._var_scale_map = collections.OrderedDict()
@@ -635,6 +672,10 @@ class QuantizationFreezePass(object):
         for op_node in ops:
             op_name = op_node.name()
             if op_name in self._quantizable_ops:
+                skipped = op_node.op().has_attr("skip_quant") and \
+                         op_node.op().attr("skip_quant")
+                if skipped:
+                    continue
                 if self._weight_quantize_type == 'channel_wise_abs_max' and op_name in self._conv_ops:
                     self._insert_post_channel_dequant_op(graph, op_node)
                 else:
@@ -727,6 +768,13 @@ class QuantizationFreezePass(object):
 
     def _insert_post_dequant_op(self, graph, op_node):
         persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
+        if len(op_node.input_arg_names()) >= 2 and len(persistable_vars) == 0:
+            raise ValueError("The op %s has more than one inputs "
+                             "and all of them are not persistable. "
+                             "Now, it is not supported!" % (op_node.name()))
+        max_range = 1
+        param_range = (1 << (self._weight_bits - 1)) - 1
+        act_range = (1 << (self._activation_bits - 1)) - 1
         for var_node in op_node.inputs:
             name = var_node.name()
             if name not in op_node.input_arg_names():
@@ -739,13 +787,12 @@ class QuantizationFreezePass(object):
             original_var_name = self._original_var_name(name)
             scale_v = self._var_scale_map[original_var_name]
             if original_var_name in persistable_vars:
-                param_range = (1 << (self._weight_bits - 1)) - 1
-                act_range = (1 << (self._activation_bits - 1)) - 1
                 assert self._is_float(
                     scale_v), 'The scale of parameter %s is not a float.' % (
                         original_var_name)
-                max_range = param_range * act_range / scale_v
+                max_range *= param_range / scale_v
             else:
+                max_range *= act_range
                 assert isinstance(scale_v, IrNode)
                 scale_var_node = self._var_scale_map[original_var_name]
 
@@ -850,7 +897,7 @@ class ConvertToInt8Pass(object):
             'The place cannot be set None.'
         self._scope = scope
         self._place = place
-        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+        self._quantizable_ops = _quantizable_op_list
 
     def apply(self, graph):
         """
@@ -866,6 +913,10 @@ class ConvertToInt8Pass(object):
         for op_node in ops:
             op_name = op_node.name()
             if op_name in self._quantizable_ops:
+                skipped = op_node.op().has_attr("skip_quant") and \
+                         op_node.op().attr("skip_quant")
+                if skipped:
+                    continue
                 for var_node in op_node.inputs:
                     name = var_node.name()
                     if name in persistable_vars:
@@ -924,14 +975,8 @@ class TransformForMobilePass(object):
     """
 
     def __init__(self):
-        self._fake_quant_op_names = [
-            'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
-            'fake_quantize_moving_average_abs_max',
-            'fake_channel_wise_quantize_abs_max'
-        ]
-        self._fake_dequant_op_names = [
-            'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
-        ]
+        self._fake_quant_op_names = _fake_quant_op_list
+        self._fake_dequant_op_names = _fake_dequant_op_list
 
     def apply(self, graph):
         """
@@ -980,12 +1025,7 @@ class ScaleForTrainingPass(object):
         self._place = place
         self._moving_rate = moving_rate
         self._is_test = None
-        self._teller_set = [
-            "mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
-            "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
-            "elementwise_add", "elementwise_mul", "dropout", "split", "prelu",
-            "conv2d_transpose", "leaky_relu"
-        ]
+        self._teller_set = _out_scale_op_list
 
     def apply(self, graph):
         """
@@ -1087,12 +1127,7 @@ class ScaleForInferencePass(object):
             scope(fluid.Scope): The scope is used to initialize these new parameters.
         """
         self._scope = scope
-        self._teller_set = [
-            "mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
-            "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
-            "elementwise_add", "elementwise_mul", "dropout", "split", "prelu",
-            "conv2d_transpose", "leaky_relu"
-        ]
+        self._teller_set = _out_scale_op_list
 
     def apply(self, graph):
         """
@@ -1135,7 +1170,7 @@ class AddQuantDequantPass(object):
         self._moving_rate = moving_rate
         self._quant_bits = quant_bits
         self._is_test = None
-        self._target_ops = ["elementwise_add", "pool2d"]
+        self._target_ops = ["elementwise_add"]
 
     def apply(self, graph):
         """

python/paddle/fluid/contrib/slim/tests/test_graph.py

@@ -123,7 +123,7 @@ class TestGraph(unittest.TestCase):
             for op in backup_graph.all_op_nodes():
                 if op.name().find('conv2d') > -1:
                     backup_marked_nodes.add(op)
-            backup_graph.draw('.', 'backup', backup_marked_nodes)
+            backup_graph.draw('./origin', 'backup', backup_marked_nodes)
         self.assertFalse(graph.has_circle())
         self.assertEqual(graph.graph_num(), 1)
         nodes = graph.topology_sort()

python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py

@@ -72,13 +72,14 @@ def residual_block(num):
     return loss
 
 
-def conv_net(img, label):
+def conv_net(img, label, quant_skip_pattern):
     conv_pool_1 = fluid.nets.simple_img_conv_pool(
         input=img,
         filter_size=5,
         num_filters=20,
         pool_size=2,
         pool_stride=2,
+        pool_type='max',
         act="relu")
     conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
@@ -87,8 +88,11 @@ def conv_net(img, label):
         num_filters=50,
         pool_size=2,
         pool_stride=2,
+        pool_type='avg',
         act="relu")
-    prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
+    hidden = fluid.layers.fc(input=conv_pool_2, size=100, act='relu')
+    with fluid.name_scope(quant_skip_pattern):
+        prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
     loss = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_loss = fluid.layers.mean(loss)
     return avg_loss
@@ -107,7 +111,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
             'mul_grad': ['X', 'Y']
         }
 
-    def check_program(self, transform_pass, program):
+    def check_program(self, program):
         quantized_ops = set()
         for block in program.blocks:
             for op in block.ops:
@@ -127,7 +131,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
                             arg_name.endswith('.quantized.dequantized'))
                         self.assertTrue(arg_name in quantized_ops)
 
-    def linear_fc_quant(self, activation_quant_type, for_ci=False):
+    def linear_fc_quant(self, activation_quant_type, for_ci=True):
         main = fluid.Program()
         startup = fluid.Program()
         with fluid.program_guard(main, startup):
@@ -135,7 +139,6 @@ class TestQuantizationTransformPass(unittest.TestCase):
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
         place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
         graph = IrGraph(core.Graph(main.desc), for_test=False)
         transform_pass = QuantizationTransformPass(
             scope=fluid.global_scope(),
@@ -150,7 +153,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
             graph.draw('.', 'quantize_fc_' + activation_quant_type,
                        marked_nodes)
         program = graph.to_program()
-        self.check_program(transform_pass, program)
+        self.check_program(program)
         val_graph = IrGraph(core.Graph(program.desc), for_test=False)
         if not for_ci:
             val_marked_nodes = set()
@@ -169,7 +172,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
     def test_linear_fc_quant_moving_average_abs_max(self):
         self.linear_fc_quant('moving_average_abs_max', for_ci=True)
 
-    def residual_block_quant(self, activation_quant_type, for_ci=False):
+    def residual_block_quant(self, activation_quant_type, for_ci=True):
         main = fluid.Program()
         startup = fluid.Program()
         with fluid.program_guard(main, startup):
@@ -177,7 +180,6 @@ class TestQuantizationTransformPass(unittest.TestCase):
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
         place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
         graph = IrGraph(core.Graph(main.desc), for_test=False)
         transform_pass = QuantizationTransformPass(
             scope=fluid.global_scope(),
@@ -192,7 +194,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
             graph.draw('.', 'quantize_residual_' + activation_quant_type,
                        marked_nodes)
         program = graph.to_program()
-        self.check_program(transform_pass, program)
+        self.check_program(program)
         val_graph = IrGraph(core.Graph(program.desc), for_test=False)
         if not for_ci:
             val_marked_nodes = set()
@@ -218,7 +220,8 @@ class TestQuantizationFreezePass(unittest.TestCase):
                      seed,
                      activation_quant_type,
                      weight_quant_type='abs_max',
-                     for_ci=False):
+                     for_ci=True,
+                     quant_skip_pattern='skip_quant'):
         def build_program(main, startup, is_test):
             main.random_seed = seed
             startup.random_seed = seed
@@ -228,7 +231,7 @@ class TestQuantizationFreezePass(unittest.TestCase):
                         name='image', shape=[1, 28, 28], dtype='float32')
                     label = fluid.layers.data(
                         name='label', shape=[1], dtype='int64')
-                    loss = conv_net(img, label)
+                    loss = conv_net(img, label, quant_skip_pattern)
                     if not is_test:
                         opt = fluid.optimizer.Adam(learning_rate=0.001)
                         opt.minimize(loss)
@@ -255,7 +258,8 @@ class TestQuantizationFreezePass(unittest.TestCase):
             scope=scope,
             place=place,
             activation_quantize_type=activation_quant_type,
-            weight_quantize_type=weight_quant_type)
+            weight_quantize_type=weight_quant_type,
+            skip_pattern=quant_skip_pattern)
         transform_pass.apply(main_graph)
         transform_pass.apply(test_graph)
         dev_name = '_gpu_' if use_cuda else '_cpu_'

python/paddle/fluid/framework.py

@@ -2728,6 +2728,8 @@ class IrGraph(object):
             if self.graph.has('__graphviz__marked_node__'):
                 self.graph.erase('__graphviz__marked_node__')
             self.graph.set('__graphviz__marked_node__', marked_nodes)
+        if not os.path.exists(save_path):
+            os.makedirs(save_path)
         viz_dot_path = os.path.join(save_path, name) + '.dot'
         viz_pass = core.get_pass('graph_viz_pass')
         viz_pass.set('graph_viz_path', viz_dot_path)